July 2017
Intermediate to advanced
398 pages
11h 54m
Chinese
Content preview from Python数据处理
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自动化和规模化
｜
313
这一进程像是一个过滤器。使用映射器说“分离文本文件中所有的单词”或者“分离过去
一个小时里推文中包含特定标签的所有用户”。下一步是
reduce
（规约）映射后的数据为
可用的形式。这类似于在第
9
章使用的聚合函数。如果我们查看所有来自
Spritzer feed
的
Twitter
句柄，可能想要根据不同的地理信息或主题统计每一个句柄的推文数量，或者所有
句柄的聚合（即，所有来自这一时区的使用这些单词最多的推文）。规约部分帮助我们处
理这些大数据，“规约”它为可阅读和可操作的报告。
正如你可能看到的，不是所有的数据集都需要
map-reduce
，而且
MapReduce
背后的理论
已经在许多
Python
数据库中可用。无论怎样，如果你有一个非常大的数据集，使用类
似
Hadoop
的
MapReduce
工具会节省大量的计算时间。如果你需要一个很棒的教程，建
议你阅读
Micheal Noll
写的关于用
Python
编写
Hadoop MapReduce
程序的指南（
http://
www.michael-noll.com/tutorials/writing-an-hadoop-mapreduce-program-in-python/
），它使用
一个单词计数程序来探索
Python
和
Hadoop
。还有很棒的关于
mrjob
（
https://pythonhosted.
org/mrjob/
）的文档，由在
Yelp
（
http://www.yelp.com
）的开发者编写和维护。如果你想
要阅读更多关于这个主题的信息，可以查看 ...
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.